






"""simple docstring"""





import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
							from PIL import Image

							from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor



@require_tokenizers
@require_vision
class       __A		(   unittest.TestCase			):

					def   lowerCamelCase__   (			self  :      Union[str, Any]					)			->							List[Any]:
											__magic_name__:	int							=       tempfile.mkdtemp()

											# fmt: off
											__magic_name__:	Dict							=       ["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing""", """,""", """low""", """lowest"""]
											# fmt: on
											__magic_name__:	str							=       os.path.join(self.tmpdirname     ,		VOCAB_FILES_NAMES["""vocab_file"""]					)
											with open(self.vocab_file     ,		"""w"""     ,		encoding="""utf-8"""					) as vocab_writer:
																	vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]					)					)

											__magic_name__:	Dict							=       {
											    """do_resize""": True,
											    """size""": {"""height""": 1_8, """width""": 1_8},
											    """do_normalize""": True,
											    """image_mean""": [0.5, 0.5, 0.5],
											    """image_std""": [0.5, 0.5, 0.5],
											}
											__magic_name__:	Union[str, Any]							=       os.path.join(self.tmpdirname     ,		__snake_case					)
											with open(self.image_processor_file     ,		"""w"""     ,		encoding="""utf-8"""					) as fp:
																	json.dump(__snake_case     ,		__snake_case					)

					def   lowerCamelCase__   (			self  :      Tuple     ,		**__snake_case  :      Dict					)			->							Tuple:
											return BertTokenizer.from_pretrained(self.tmpdirname     ,		**__snake_case					)

					def   lowerCamelCase__   (			self  :      List[Any]     ,		**__snake_case  :      Optional[Any]					)			->							Optional[int]:
											return ViTImageProcessor.from_pretrained(self.tmpdirname     ,		**__snake_case					)

					def   lowerCamelCase__   (			self  :      Optional[int]					)			->							Dict:
											shutil.rmtree(self.tmpdirname					)

					def   lowerCamelCase__   (			self  :      Optional[Any]					)			->							Union[str, Any]:
											__magic_name__:	int							=       [np.random.randint(2_5_5     ,		size=(3, 3_0, 4_0_0)     ,		dtype=np.uinta					)]

											__magic_name__:	List[str]							=       [Image.fromarray(np.moveaxis(__snake_case     ,		0     ,		-1					)					) for x in image_inputs]

											return image_inputs

					def   lowerCamelCase__   (			self  :      Optional[int]					)			->							int:
											__magic_name__:	List[Any]							=       self.get_tokenizer()
											__magic_name__:	Optional[Any]							=       self.get_image_processor()

											__magic_name__:	List[Any]							=       VisionTextDualEncoderProcessor(tokenizer=__snake_case     ,		image_processor=__snake_case					)

											processor.save_pretrained(self.tmpdirname					)
											__magic_name__:	int							=       VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname					)

											self.assertEqual(processor.tokenizer.get_vocab()     ,		tokenizer.get_vocab()					)
											self.assertIsInstance(processor.tokenizer     ,		(BertTokenizer, BertTokenizerFast)					)

											self.assertEqual(processor.image_processor.to_json_string()     ,		image_processor.to_json_string()					)
											self.assertIsInstance(processor.image_processor     ,		__snake_case					)

					def   lowerCamelCase__   (			self  :      Any					)			->							Optional[Any]:
											__magic_name__:	Tuple							=       VisionTextDualEncoderProcessor(
											    tokenizer=self.get_tokenizer()     ,		image_processor=self.get_image_processor()					)
											processor.save_pretrained(self.tmpdirname					)

											__magic_name__:	Dict							=       self.get_tokenizer(bos_token="""(BOS)"""     ,		eos_token="""(EOS)"""					)
											__magic_name__:	str							=       self.get_image_processor(do_normalize=__snake_case     ,		padding_value=1.0					)

											__magic_name__:	Any							=       VisionTextDualEncoderProcessor.from_pretrained(
											    self.tmpdirname     ,		bos_token="""(BOS)"""     ,		eos_token="""(EOS)"""     ,		do_normalize=__snake_case     ,		padding_value=1.0					)

											self.assertEqual(processor.tokenizer.get_vocab()     ,		tokenizer_add_kwargs.get_vocab()					)
											self.assertIsInstance(processor.tokenizer     ,		(BertTokenizer, BertTokenizerFast)					)

											self.assertEqual(processor.image_processor.to_json_string()     ,		image_processor_add_kwargs.to_json_string()					)
											self.assertIsInstance(processor.image_processor     ,		__snake_case					)

					def   lowerCamelCase__   (			self  :      str					)			->							Union[str, Any]:
											__magic_name__:	Union[str, Any]							=       self.get_image_processor()
											__magic_name__:	Dict							=       self.get_tokenizer()

											__magic_name__:	List[str]							=       VisionTextDualEncoderProcessor(tokenizer=__snake_case     ,		image_processor=__snake_case					)

											__magic_name__:	Optional[Any]							=       self.prepare_image_inputs()

											__magic_name__:	Any							=       image_processor(__snake_case     ,		return_tensors="""np"""					)
											__magic_name__:	List[str]							=       processor(images=__snake_case     ,		return_tensors="""np"""					)

											for key in input_feat_extract.keys():
																	self.assertAlmostEqual(input_feat_extract[key].sum()     ,		input_processor[key].sum()     ,		delta=1E-2					)

					def   lowerCamelCase__   (			self  :      Optional[Any]					)			->							List[str]:
											__magic_name__:	Dict							=       self.get_image_processor()
											__magic_name__:	Optional[Any]							=       self.get_tokenizer()

											__magic_name__:	Dict							=       VisionTextDualEncoderProcessor(tokenizer=__snake_case     ,		image_processor=__snake_case					)

											__magic_name__:	Union[str, Any]							=       """lower newer"""

											__magic_name__:	Union[str, Any]							=       processor(text=__snake_case					)

											__magic_name__:	List[Any]							=       tokenizer(__snake_case					)

											for key in encoded_tok.keys():
																	self.assertListEqual(encoded_tok[key]     ,		encoded_processor[key]					)

					def   lowerCamelCase__   (			self  :      Optional[Any]					)			->							List[str]:
											__magic_name__:	Optional[Any]							=       self.get_image_processor()
											__magic_name__:	Optional[Any]							=       self.get_tokenizer()

											__magic_name__:	Tuple							=       VisionTextDualEncoderProcessor(tokenizer=__snake_case     ,		image_processor=__snake_case					)

											__magic_name__:	Union[str, Any]							=       """lower newer"""
											__magic_name__:	Optional[int]							=       self.prepare_image_inputs()

											__magic_name__:	Optional[Any]							=       processor(text=__snake_case     ,		images=__snake_case					)

											self.assertListEqual(list(inputs.keys()					)     ,		["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""]					)

											# test if it raises when no input is passed
											with self.assertRaises(__snake_case					):
																	processor()

					def   lowerCamelCase__   (			self  :      Dict					)			->							Dict:
											__magic_name__:	Tuple							=       self.get_image_processor()
											__magic_name__:	Any							=       self.get_tokenizer()

											__magic_name__:	Dict							=       VisionTextDualEncoderProcessor(tokenizer=__snake_case     ,		image_processor=__snake_case					)

											__magic_name__:	Optional[Any]							=       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

											__magic_name__:	Optional[int]							=       processor.batch_decode(__snake_case					)
											__magic_name__:	int							=       tokenizer.batch_decode(__snake_case					)

											self.assertListEqual(__snake_case     ,		__snake_case					)






					def   lowerCamelCase__   (			self  :      int					)			->							List[str]:
											__magic_name__:	Optional[Any]							=       self.get_image_processor()
											__magic_name__:	Optional[Any]							=       self.get_tokenizer()

											__magic_name__:	Optional[int]							=       VisionTextDualEncoderProcessor(tokenizer=__snake_case     ,		image_processor=__snake_case					)

											__magic_name__:	Optional[Any]							=       """lower newer"""
											__magic_name__:	Optional[int]							=       self.prepare_image_inputs()

											__magic_name__:	Optional[Any]							=       processor(text=__snake_case     ,		images=__snake_case					)

											self.assertListEqual(list(inputs.keys()					)     ,		processor.model_input_names					)




import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
     import torch

     from transformers.generation import (
         MaxLengthCriteria,
         MaxNewTokensCriteria,
         MaxTimeCriteria,
         StoppingCriteriaList,
         validate_stopping_criteria,
     )
@require_torch
class SCREAMING_SNAKE_CASE_		(      unittest.TestCase		):







   def 						lowerCamelCase_	(     self						:						Tuple   ,   lowerCamelCase_						:						Tuple				):





    """simple docstring"""


    UpperCamelCase			  =      3
    UpperCamelCase			  =      250

    UpperCamelCase			  =      ids_tensor((batch_size, length)   ,   lowerCamelCase_				)
    UpperCamelCase			  =      torch.ones((batch_size, length)   ,   device=lowerCamelCase_   ,   dtype=torch.float				) / length
    return input_ids, scores







   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(5				)

    UpperCamelCase			  =      StoppingCriteriaList(
        [
            MaxLengthCriteria(max_length=10				),
            MaxTimeCriteria(max_time=0.1				),
        ]				)

    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(9				)
    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(10				)
    self.assertTrue(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    UpperCamelCase			  =      MaxLengthCriteria(max_length=10				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(5				)
    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(9				)
    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(10				)
    self.assertTrue(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    UpperCamelCase			  =      MaxNewTokensCriteria(start_length=5   ,   max_new_tokens=5				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(5				)
    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(9				)
    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(10				)
    self.assertTrue(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase			  =      StoppingCriteriaList([criteria]				)
    self.assertEqual(criteria_list.max_length   ,   10				)







   def 						lowerCamelCase_	(     self						:						Optional[int]				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(5				)

    UpperCamelCase			  =      MaxTimeCriteria(max_time=0.1				)
    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase			  =      MaxTimeCriteria(max_time=0.1   ,   initial_timestamp=time.time() - 0.2				)
    self.assertTrue(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10				)]				)   ,   10				)

    with self.assertWarns(lowerCamelCase_				):
     validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10				)]				)   ,   11				)

    UpperCamelCase			  =      validate_stopping_criteria(StoppingCriteriaList()   ,   11				)

    self.assertEqual(len(lowerCamelCase_				)   ,   1				)
